Nonlinear unmixing of hyperspectral images based on multi-kernel learning

Jie Chen, Cédric Richard, Paul Honeine

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

Abstract

Nonlinear unmixing of hyperspectral images has generated considerable interest among researchers, as it may overcome some inherent limitations of the linear mixing model. In this paper, we formulate the problem of estimating abundances of a nonlinear mixture of hyperspectral data based on a new multi-kernel learning paradigm. Experiments are conducted using both synthetic and real images in order to illustrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2012 4th Workshop on Hyperspectral Image and Signal Processing, WHISPERS 2012
PublisherIEEE Computer Society
ISBN (Print)9781479934065
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 4th Workshop on Hyperspectral Image and Signal Processing, WHISPERS 2012 - Shanghai, China
Duration: 4 Jun 20127 Jun 2012

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
ISSN (Print)2158-6276

Conference

Conference2012 4th Workshop on Hyperspectral Image and Signal Processing, WHISPERS 2012
Country/TerritoryChina
CityShanghai
Period4/06/127/06/12

Keywords

  • Hyperspectral image
  • multi-kernel learning
  • nonlinear unmixing

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